In the case of gender, there is typically no natural reason to code the variable female = 0, male = 1, versus male = 0, female = 1. However, convention may suggest one coding is more familiar to a reader; or choosing a coding that makes the regression coefficient positive …

We will begin with a brief discussion of the options constructing 0/1 variables, including selection of data type and the implications for the coding of missing values.

In this section, we focus on issues concerning the coding of qualitative variables. In particular, we: In the birth weight example, we coded the qualitative variable Smoking by creating a (0, 1) indicator variable that took on the value 1 for smoking mothers and 0 for non-smoking mothers. What if we had instead tried to use two indicator variables?

0/1 is what's used for binary coding, since you can then use it both in terms of numerical and categorical values. As for is 0 man or woman, it has no impact on the results, simply on the interpretation of the results. 0 is the baseline and 1 is the "additional effect".

On this webpage we show how to create the dummy coding using worksheetl functions found in the Real Statistics Resource Pack. We also demonstrate an even more straightforward coding, called the simple coding, where categories such as Democrat, Republican, Independent are coded as 0, 1, 2.

For binary/ dichotomous (categorical) classification, the usual practice (for coding) is to go for 0 & 1. But absolutely no problem, if it is started with 1 as base (1 & 2).

Categorical independent variables can be used in a regression analysis, but first, they need to be coded by one or more dummy variables (also called tag variables). Each such dummy variable will only take the value 0 or 1 (although in ANOVA using Regression, we describe an alternative coding that takes values 0, 1, or -1).

This chapter introduces you to the process of qualitative data analysis and the basic concept of coding, while the following chapter (chapter 19) will take you further into the various kinds of codes and how to use them effectively.

Not only is it okay to code it as 0/1, it's . Correct. This is not a problem. The intercept has the mean for the 0 category, and the coefficient for the 0/1 variable has the difference in means between the two categories. The p-value for this variable shouldn't change from such recoding.

Dichotomous coding: This involves assigning a binary code (e.g., 0 or 1) to data items. For example, responses to a survey question about gender might be coded as follows: Male = 0, Female = 1. Numeric coding: This involves assigning numerical values to data items.